Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Electrical and Computer Engineering

Supervisor

Shami, Abdallah

2nd Supervisor

Hussein, Ahmed R.

Affiliation

University of Guelph

Co-Supervisor

Abstract

The communications industry continues to evolve to meet the ever-growing demands of fast connectivity and higher energy-efficiency and has emerged the concept of Internet of Things (IoT) systems. IoT devices can be run on Wi-Fi or cellular network, helping businesses to receive higher return on investments.

As billions of devices on cellular networks operate on the limited licensed spectrum, it is becoming scarcer. Mobile network operators are investigating to access the immense unlicensed spectrum, on which Wi-Fi is prominently operated. Managing this coexistence between the cellular and Wi-Fi networks poses several challenges.

One challenge is the spectrum sharing that affects the network capacity and the spectrum efficiency by properly allocating the available resources for each technology. A second challenge is to maintain the quality of service (QoS) while maximizing the aggregated throughput. A final challenge is to reduce the power consumption of cellular base stations by creating a sleep/wakeup policy, thereby lowering the capital and operating expenses for the mobile network operators.

To this end, this thesis proposes various optimization modeling for the coexistence mechanisms in the unlicensed spectrum, as well as intelligent techniques to manage the increasing power consumption with increased usage. First, this thesis develops optimization modeling techniques to properly allocate resources for the coexistence of the Wi-Fi and cellular networks by improving the aggregate throughput, while maintaining the minimum required power consumption. Next, this thesis implements the coexistence mechanism by simulating real-time traffic information to maximize the aggregate throughput, while satisfying the QoS for each user. Finally, this thesis investigates the use of machine learning techniques to predict the traffic behaviour of base stations; this will determine the sleep/wakeup schedule, thereby minimizing the power consumption while maintaining the QoS for each cellular user.

Summary for Lay Audience

The amount of devices that connect to the Internet are exponentially increasing that users are now competing with each other to access the limited cellular network. To solve the cellular spectrum scarcity while users are demanding faster connectivity as well as higher energy-efficiency, network operators are investigating the unlicensed spectrum.

The main challenge of network operators shifting their focus on utilizing the unlicensed spectrum, which is prominently used by Wi-Fi, is that the cellular devices overpower the Wi-Fi systems, not allowing the Wi-Fi technology to be accessible.

This thesis proposes a coexistence mechanism to allow the cellular system to communicate alongside with the Wi-Fi systems without degrading the performance, as well as intelligent techniques to manage the increasing power consumption with increasing usage.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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